(R1). Here we analyze the forecasting results on the different datasets at horizons 3, 6, and 12, respectively. For reference, we also add a vanilla LSTM [1]} baseline that jointly forecasts all timeseries, as well LSTM-U, which consists of \(N\)  univariate LSTMs. Essentially, the LSTM uses information from all timeseries, though lacks typical GNN properties such as permutation equivariance and does not leverage sparsity. LSTM-U is on the other end of the spectrum and simply views all timeseries as completely independent. In Table REF  (appendix) we present the results.
